Overview

Dataset statistics

Number of variables32
Number of observations387840
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.7 MiB
Average record size in memory256.0 B

Variable types

Numeric15
Categorical17

Alerts

NEMUERTO has constant value "0"Constant
NEHERIDO has constant value "0"Constant
Municipio_Nombre has a high cardinality: 72 distinct valuesHigh cardinality
Fecha_Formated has a high cardinality: 341656 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with ANIO and 1 other fieldsHigh correlation
ID_MUNICIPIO is highly overall correlated with Municipio_NombreHigh correlation
ANIO is highly overall correlated with Unnamed: 0High correlation
ID_EDAD is highly overall correlated with SEXO and 1 other fieldsHigh correlation
CONDMUERTO is highly overall correlated with Total_MuertosHigh correlation
CONDHERIDO is highly overall correlated with Total_HeridosHigh correlation
PASAMUERTO is highly overall correlated with Total_MuertosHigh correlation
PASAHERIDO is highly overall correlated with Total_HeridosHigh correlation
Total_Muertos is highly overall correlated with CONDMUERTO and 1 other fieldsHigh correlation
Total_Heridos is highly overall correlated with CONDHERIDO and 1 other fieldsHigh correlation
URBANA is highly overall correlated with SUBURBANA and 2 other fieldsHigh correlation
SUBURBANA is highly overall correlated with URBANA and 1 other fieldsHigh correlation
TIPACCID is highly overall correlated with URBANA and 8 other fieldsHigh correlation
CAUSAACCI is highly overall correlated with TIPACCID and 5 other fieldsHigh correlation
CAPAROD is highly overall correlated with DIASEMANA and 6 other fieldsHigh correlation
SEXO is highly overall correlated with TIPACCID and 6 other fieldsHigh correlation
ALIENTO is highly overall correlated with TIPACCID and 6 other fieldsHigh correlation
CINTURON is highly overall correlated with Unnamed: 0 and 6 other fieldsHigh correlation
Municipio_Nombre is highly overall correlated with ID_MUNICIPIO and 8 other fieldsHigh correlation
PEATMUERTO is highly overall correlated with Total_MuertosHigh correlation
DIASEMANA is highly overall correlated with CAPARODHigh correlation
PEATHERIDO is highly overall correlated with TIPACCIDHigh correlation
CICLHERIDO is highly overall correlated with TIPACCIDHigh correlation
PASAMUERTO is highly skewed (γ1 = 36.22011675)Skewed
OTROHERIDO is highly skewed (γ1 = 31.44966803)Skewed
Fecha_Formated is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
ID_HORA has 11519 (3.0%) zerosZeros
ID_EDAD has 44618 (11.5%) zerosZeros
CONDMUERTO has 385345 (99.4%) zerosZeros
CONDHERIDO has 322149 (83.1%) zerosZeros
PASAMUERTO has 386408 (99.6%) zerosZeros
PASAHERIDO has 342606 (88.3%) zerosZeros
PEATHERIDO has 365679 (94.3%) zerosZeros
OTROHERIDO has 386332 (99.6%) zerosZeros
Total_Muertos has 382142 (98.5%) zerosZeros
Total_Heridos has 265447 (68.4%) zerosZeros

Reproduction

Analysis started2022-12-12 22:23:02.794372
Analysis finished2022-12-12 22:26:37.862736
Duration3 minutes and 35.07 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct387840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4706519.6
Minimum207539
Maximum9511225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:38.083735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum207539
5-th percentile473219.95
Q11823126.8
median4542037.5
Q37701589.2
95-th percentile9491833.1
Maximum9511225
Range9303686
Interquartile range (IQR)5878462.5

Descriptive statistics

Standard deviation2989350.8
Coefficient of variation (CV)0.63515105
Kurtosis-1.3350588
Mean4706519.6
Median Absolute Deviation (MAD)2730997
Skewness0.098717642
Sum1.8253765 × 1012
Variance8.9362184 × 1012
MonotonicityStrictly increasing
2022-12-12T15:26:38.319738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207539 1
 
< 0.1%
6579710 1
 
< 0.1%
6579719 1
 
< 0.1%
6579718 1
 
< 0.1%
6579717 1
 
< 0.1%
6579716 1
 
< 0.1%
6579715 1
 
< 0.1%
6579714 1
 
< 0.1%
6579713 1
 
< 0.1%
6579712 1
 
< 0.1%
Other values (387830) 387830
> 99.9%
ValueCountFrequency (%)
207539 1
< 0.1%
207540 1
< 0.1%
207541 1
< 0.1%
207542 1
< 0.1%
207543 1
< 0.1%
207544 1
< 0.1%
207545 1
< 0.1%
207546 1
< 0.1%
207547 1
< 0.1%
207548 1
< 0.1%
ValueCountFrequency (%)
9511225 1
< 0.1%
9511224 1
< 0.1%
9511223 1
< 0.1%
9511222 1
< 0.1%
9511221 1
< 0.1%
9511220 1
< 0.1%
9511219 1
< 0.1%
9511218 1
< 0.1%
9511217 1
< 0.1%
9511216 1
< 0.1%

ID_MUNICIPIO
Real number (ℝ)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.586971
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:38.579733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q118
median30
Q343
95-th percentile55
Maximum72
Range71
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.278054
Coefficient of variation (CV)0.45202351
Kurtosis-0.24425264
Mean31.586971
Median Absolute Deviation (MAD)12
Skewness0.52653435
Sum12250691
Variance203.86282
MonotonicityNot monotonic
2022-12-12T15:26:38.821739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 96495
24.9%
18 74650
19.2%
55 45061
11.6%
29 30280
 
7.8%
43 28059
 
7.2%
17 27470
 
7.1%
48 14564
 
3.8%
42 14214
 
3.7%
2 7105
 
1.8%
25 6886
 
1.8%
Other values (62) 43056
11.1%
ValueCountFrequency (%)
1 135
 
< 0.1%
2 7105
1.8%
3 934
 
0.2%
4 1430
 
0.4%
5 156
 
< 0.1%
6 568
 
0.1%
7 95
 
< 0.1%
8 57
 
< 0.1%
9 127
 
< 0.1%
10 123
 
< 0.1%
ValueCountFrequency (%)
72 1472
0.4%
71 2303
0.6%
70 1837
0.5%
69 194
 
0.1%
68 175
 
< 0.1%
67 183
 
< 0.1%
66 783
 
0.2%
65 138
 
< 0.1%
64 98
 
< 0.1%
63 105
 
< 0.1%

ANIO
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.7007
Minimum1997
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:39.068735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile1998
Q12002
median2008
Q32016
95-th percentile2021
Maximum2021
Range24
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.387483
Coefficient of variation (CV)0.0036777421
Kurtosis-1.2338869
Mean2008.7007
Median Absolute Deviation (MAD)6
Skewness0.13193145
Sum7.7905447 × 108
Variance54.574905
MonotonicityIncreasing
2022-12-12T15:26:39.263733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2021 21209
 
5.5%
2006 18644
 
4.8%
2005 18119
 
4.7%
2001 17642
 
4.5%
2007 17372
 
4.5%
2004 17321
 
4.5%
2019 17074
 
4.4%
1998 16955
 
4.4%
2000 16811
 
4.3%
2002 16749
 
4.3%
Other values (15) 209944
54.1%
ValueCountFrequency (%)
1997 15499
4.0%
1998 16955
4.4%
1999 15799
4.1%
2000 16811
4.3%
2001 17642
4.5%
2002 16749
4.3%
2003 16627
4.3%
2004 17321
4.5%
2005 18119
4.7%
2006 18644
4.8%
ValueCountFrequency (%)
2021 21209
5.5%
2020 13362
3.4%
2019 17074
4.4%
2018 16369
4.2%
2017 15927
4.1%
2016 13145
3.4%
2015 11960
3.1%
2014 12604
3.2%
2013 10451
2.7%
2012 11945
3.1%

MES
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6294761
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:39.464736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4687015
Coefficient of variation (CV)0.52322408
Kurtosis-1.2217945
Mean6.6294761
Median Absolute Deviation (MAD)3
Skewness-0.023430939
Sum2571176
Variance12.03189
MonotonicityNot monotonic
2022-12-12T15:26:39.627738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 37170
9.6%
5 33637
8.7%
10 33554
8.7%
3 33337
8.6%
6 33013
8.5%
11 32692
8.4%
9 31592
8.1%
7 31466
8.1%
8 30986
8.0%
4 30358
7.8%
Other values (2) 60035
15.5%
ValueCountFrequency (%)
1 30270
7.8%
2 29765
7.7%
3 33337
8.6%
4 30358
7.8%
5 33637
8.7%
6 33013
8.5%
7 31466
8.1%
8 30986
8.0%
9 31592
8.1%
10 33554
8.7%
ValueCountFrequency (%)
12 37170
9.6%
11 32692
8.4%
10 33554
8.7%
9 31592
8.1%
8 30986
8.0%
7 31466
8.1%
6 33013
8.5%
5 33637
8.7%
4 30358
7.8%
3 33337
8.6%

ID_HORA
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.220001
Minimum0
Maximum23
Zeros11519
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:39.800738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median14
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.2857556
Coefficient of variation (CV)0.47547317
Kurtosis-0.73017319
Mean13.220001
Median Absolute Deviation (MAD)5
Skewness-0.4350823
Sum5127245
Variance39.510723
MonotonicityNot monotonic
2022-12-12T15:26:39.979738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18 25001
 
6.4%
17 23832
 
6.1%
19 22869
 
5.9%
13 22201
 
5.7%
14 22188
 
5.7%
16 22171
 
5.7%
15 21715
 
5.6%
20 20705
 
5.3%
12 19468
 
5.0%
11 17078
 
4.4%
Other values (14) 170612
44.0%
ValueCountFrequency (%)
0 11519
3.0%
1 10427
2.7%
2 10958
2.8%
3 9535
2.5%
4 6529
1.7%
5 5067
 
1.3%
6 8382
2.2%
7 13653
3.5%
8 16229
4.2%
9 14690
3.8%
ValueCountFrequency (%)
23 15040
3.9%
22 15488
4.0%
21 16762
4.3%
20 20705
5.3%
19 22869
5.9%
18 25001
6.4%
17 23832
6.1%
16 22171
5.7%
15 21715
5.6%
14 22188
5.7%

ID_DIA
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.580531
Minimum0
Maximum32
Zeros431
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:40.207736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum32
Range32
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8070287
Coefficient of variation (CV)0.56525858
Kurtosis-1.1732797
Mean15.580531
Median Absolute Deviation (MAD)8
Skewness0.016990815
Sum6042753
Variance77.563754
MonotonicityNot monotonic
2022-12-12T15:26:40.437735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
15 16558
 
4.3%
1 14780
 
3.8%
28 13511
 
3.5%
14 12926
 
3.3%
16 12924
 
3.3%
2 12763
 
3.3%
25 12744
 
3.3%
3 12697
 
3.3%
8 12676
 
3.3%
17 12671
 
3.3%
Other values (23) 253590
65.4%
ValueCountFrequency (%)
0 431
 
0.1%
1 14780
3.8%
2 12763
3.3%
3 12697
3.3%
4 12629
3.3%
5 12342
3.2%
6 12565
3.2%
7 12364
3.2%
8 12676
3.3%
9 12413
3.2%
ValueCountFrequency (%)
32 2
 
< 0.1%
31 7196
1.9%
30 11452
3.0%
29 11431
2.9%
28 13511
3.5%
27 11968
3.1%
26 12000
3.1%
25 12744
3.3%
24 12225
3.2%
23 12258
3.2%

DIASEMANA
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Sábado
70220 
Domingo
70205 
Viernes
56146 
Lunes
52248 
Jueves
47393 
Other values (4)
91628 

Length

Max length16
Median length15
Mean length6.5544606
Min length5

Characters and Unicode

Total characters2542082
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowViernes
2nd rowMiércoles
3rd rowJueves
4th rowMartes
5th rowLunes

Common Values

ValueCountFrequency (%)
Sábado 70220
18.1%
Domingo 70205
18.1%
Viernes 56146
14.5%
Lunes 52248
13.5%
Jueves 47393
12.2%
Martes 45658
11.8%
Miércoles 45537
11.7%
Certificado cero 431
 
0.1%
No especificado 2
 
< 0.1%

Length

2022-12-12T15:26:40.786736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:41.850739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
sábado 70220
18.1%
domingo 70205
18.1%
viernes 56146
14.5%
lunes 52248
13.5%
jueves 47393
12.2%
martes 45658
11.8%
miércoles 45537
11.7%
certificado 431
 
0.1%
cero 431
 
0.1%
no 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 351387
13.8%
o 257033
 
10.1%
s 246984
 
9.7%
n 178599
 
7.0%
i 172754
 
6.8%
r 148203
 
5.8%
a 116311
 
4.6%
u 99641
 
3.9%
M 91195
 
3.6%
d 70653
 
2.8%
Other values (19) 809322
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2153809
84.7%
Uppercase Letter 387840
 
15.3%
Space Separator 433
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 351387
16.3%
o 257033
11.9%
s 246984
11.5%
n 178599
8.3%
i 172754
 
8.0%
r 148203
 
6.9%
a 116311
 
5.4%
u 99641
 
4.6%
d 70653
 
3.3%
á 70220
 
3.3%
Other values (10) 442024
20.5%
Uppercase Letter
ValueCountFrequency (%)
M 91195
23.5%
S 70220
18.1%
D 70205
18.1%
V 56146
14.5%
L 52248
13.5%
J 47393
12.2%
C 431
 
0.1%
N 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2541649
> 99.9%
Common 433
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 351387
13.8%
o 257033
 
10.1%
s 246984
 
9.7%
n 178599
 
7.0%
i 172754
 
6.8%
r 148203
 
5.8%
a 116311
 
4.6%
u 99641
 
3.9%
M 91195
 
3.6%
d 70653
 
2.8%
Other values (18) 808889
31.8%
Common
ValueCountFrequency (%)
433
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2426325
95.4%
None 115757
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 351387
14.5%
o 257033
 
10.6%
s 246984
 
10.2%
n 178599
 
7.4%
i 172754
 
7.1%
r 148203
 
6.1%
a 116311
 
4.8%
u 99641
 
4.1%
M 91195
 
3.8%
d 70653
 
2.9%
Other values (17) 693565
28.6%
None
ValueCountFrequency (%)
á 70220
60.7%
é 45537
39.3%

URBANA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Accidente en intersección
299612 
Accidente en no intersección
53408 
Sin accidente en esta zona
34820 

Length

Max length28
Median length25
Mean length25.502898
Min length25

Characters and Unicode

Total characters9891044
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSin accidente en esta zona
2nd rowSin accidente en esta zona
3rd rowSin accidente en esta zona
4th rowAccidente en no intersección
5th rowAccidente en intersección

Common Values

ValueCountFrequency (%)
Accidente en intersección 299612
77.3%
Accidente en no intersección 53408
 
13.8%
Sin accidente en esta zona 34820
 
9.0%

Length

2022-12-12T15:26:42.417746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:42.635735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
accidente 387840
30.1%
en 387840
30.1%
intersección 353020
27.4%
no 53408
 
4.2%
sin 34820
 
2.7%
esta 34820
 
2.7%
zona 34820
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 1904380
19.3%
n 1604768
16.2%
c 1481720
15.0%
i 1128700
11.4%
898728
9.1%
t 775680
7.8%
d 387840
 
3.9%
s 387840
 
3.9%
A 353020
 
3.6%
r 353020
 
3.6%
Other values (5) 615348
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8604476
87.0%
Space Separator 898728
 
9.1%
Uppercase Letter 387840
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1904380
22.1%
n 1604768
18.7%
c 1481720
17.2%
i 1128700
13.1%
t 775680
9.0%
d 387840
 
4.5%
s 387840
 
4.5%
r 353020
 
4.1%
ó 353020
 
4.1%
a 104460
 
1.2%
Other values (2) 123048
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
A 353020
91.0%
S 34820
 
9.0%
Space Separator
ValueCountFrequency (%)
898728
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8992316
90.9%
Common 898728
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1904380
21.2%
n 1604768
17.8%
c 1481720
16.5%
i 1128700
12.6%
t 775680
8.6%
d 387840
 
4.3%
s 387840
 
4.3%
A 353020
 
3.9%
r 353020
 
3.9%
ó 353020
 
3.9%
Other values (4) 262328
 
2.9%
Common
ValueCountFrequency (%)
898728
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9538024
96.4%
None 353020
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1904380
20.0%
n 1604768
16.8%
c 1481720
15.5%
i 1128700
11.8%
898728
9.4%
t 775680
8.1%
d 387840
 
4.1%
s 387840
 
4.1%
A 353020
 
3.7%
r 353020
 
3.7%
Other values (4) 262328
 
2.8%
None
ValueCountFrequency (%)
ó 353020
100.0%

SUBURBANA
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Sin accidente en esta zona
354836 
Accidente en camino rural
 
15712
Accidente en carretera estatal
 
13121
Accidentes en otro camino
 
4171

Length

Max length30
Median length26
Mean length26.084058
Min length25

Characters and Unicode

Total characters10116441
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccidente en carretera estatal
2nd rowAccidente en camino rural
3rd rowAccidente en camino rural
4th rowSin accidente en esta zona
5th rowSin accidente en esta zona

Common Values

ValueCountFrequency (%)
Sin accidente en esta zona 354836
91.5%
Accidente en camino rural 15712
 
4.1%
Accidente en carretera estatal 13121
 
3.4%
Accidentes en otro camino 4171
 
1.1%

Length

2022-12-12T15:26:42.831750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:43.050776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
en 387840
20.3%
accidente 383669
20.1%
sin 354836
18.6%
esta 354836
18.6%
zona 354836
18.6%
camino 19883
 
1.0%
rural 15712
 
0.8%
carretera 13121
 
0.7%
estatal 13121
 
0.7%
accidentes 4171
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 1557719
15.4%
1518356
15.0%
n 1505235
14.9%
a 1152587
11.4%
c 808684
8.0%
t 786210
7.8%
i 762559
7.5%
d 387840
 
3.8%
o 383061
 
3.8%
s 372128
 
3.7%
Other values (7) 882062
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8210245
81.2%
Space Separator 1518356
 
15.0%
Uppercase Letter 387840
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1557719
19.0%
n 1505235
18.3%
a 1152587
14.0%
c 808684
9.8%
t 786210
9.6%
i 762559
9.3%
d 387840
 
4.7%
o 383061
 
4.7%
s 372128
 
4.5%
z 354836
 
4.3%
Other values (4) 139386
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
S 354836
91.5%
A 33004
 
8.5%
Space Separator
ValueCountFrequency (%)
1518356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8598085
85.0%
Common 1518356
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1557719
18.1%
n 1505235
17.5%
a 1152587
13.4%
c 808684
9.4%
t 786210
9.1%
i 762559
8.9%
d 387840
 
4.5%
o 383061
 
4.5%
s 372128
 
4.3%
S 354836
 
4.1%
Other values (6) 527226
 
6.1%
Common
ValueCountFrequency (%)
1518356
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10116441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1557719
15.4%
1518356
15.0%
n 1505235
14.9%
a 1152587
11.4%
c 808684
8.0%
t 786210
7.8%
i 762559
7.5%
d 387840
 
3.8%
o 383061
 
3.8%
s 372128
 
3.7%
Other values (7) 882062
8.7%

TIPACCID
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Colisión con vehículo automotor
255587 
Colisión con objeto fijo
44410 
Colisión con peatón (atropellamiento)
 
22067
Colisión con motocicleta
 
20390
Volcadura
 
13726
Other values (8)
31660 

Length

Max length37
Median length31
Mean length28.26537
Min length4

Characters and Unicode

Total characters10962441
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColisión con vehículo automotor
2nd rowVolcadura
3rd rowColisión con animal
4th rowColisión con objeto fijo
5th rowColisión con objeto fijo

Common Values

ValueCountFrequency (%)
Colisión con vehículo automotor 255587
65.9%
Colisión con objeto fijo 44410
 
11.5%
Colisión con peatón (atropellamiento) 22067
 
5.7%
Colisión con motocicleta 20390
 
5.3%
Volcadura 13726
 
3.5%
Colisión con ciclista 10244
 
2.6%
Salida del camino 9330
 
2.4%
Caída de pasajero 4450
 
1.1%
Otro 2929
 
0.8%
Colisión con animal 2513
 
0.6%
Other values (3) 2194
 
0.6%

Length

2022-12-12T15:26:43.260736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
colisión 355405
24.5%
con 355405
24.5%
vehículo 255587
17.6%
automotor 255587
17.6%
objeto 44410
 
3.1%
fijo 44410
 
3.1%
peatón 22067
 
1.5%
atropellamiento 22067
 
1.5%
motocicleta 20390
 
1.4%
volcadura 13726
 
0.9%
Other values (13) 61036
 
4.2%

Most occurring characters

ValueCountFrequency (%)
o 1985747
18.1%
1062250
 
9.7%
i 843348
 
7.7%
n 767155
 
7.0%
l 720853
 
6.6%
c 699326
 
6.4%
t 677554
 
6.2%
u 524900
 
4.8%
a 432700
 
3.9%
e 408828
 
3.7%
Other values (19) 2839780
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9468217
86.4%
Space Separator 1062250
 
9.7%
Uppercase Letter 387840
 
3.5%
Open Punctuation 22067
 
0.2%
Close Punctuation 22067
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1985747
21.0%
i 843348
8.9%
n 767155
 
8.1%
l 720853
 
7.6%
c 699326
 
7.4%
t 677554
 
7.2%
u 524900
 
5.5%
a 432700
 
4.6%
e 408828
 
4.3%
ó 377472
 
4.0%
Other values (11) 2030334
21.4%
Uppercase Letter
ValueCountFrequency (%)
C 361671
93.3%
V 13726
 
3.5%
S 9330
 
2.4%
O 2929
 
0.8%
I 184
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1062250
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22067
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22067
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9856057
89.9%
Common 1106384
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1985747
20.1%
i 843348
 
8.6%
n 767155
 
7.8%
l 720853
 
7.3%
c 699326
 
7.1%
t 677554
 
6.9%
u 524900
 
5.3%
a 432700
 
4.4%
e 408828
 
4.1%
ó 377472
 
3.8%
Other values (16) 2418174
24.5%
Common
ValueCountFrequency (%)
1062250
96.0%
( 22067
 
2.0%
) 22067
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10324932
94.2%
None 637509
 
5.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1985747
19.2%
1062250
10.3%
i 843348
 
8.2%
n 767155
 
7.4%
l 720853
 
7.0%
c 699326
 
6.8%
t 677554
 
6.6%
u 524900
 
5.1%
a 432700
 
4.2%
e 408828
 
4.0%
Other values (17) 2202271
21.3%
None
ValueCountFrequency (%)
ó 377472
59.2%
í 260037
40.8%

CAUSAACCI
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Conductor
307451 
Otra
63302 
Falla del vehículo
 
5512
Mala condición del camino
 
4894
Peatón o pasajero
 
4865

Length

Max length25
Median length9
Mean length8.6468492
Min length4

Characters and Unicode

Total characters3353594
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConductor
2nd rowConductor
3rd rowOtra
4th rowConductor
5th rowConductor

Common Values

ValueCountFrequency (%)
Conductor 307451
79.3%
Otra 63302
 
16.3%
Falla del vehículo 5512
 
1.4%
Mala condición del camino 4894
 
1.3%
Peatón o pasajero 4865
 
1.3%
Certificado cero 1816
 
0.5%

Length

2022-12-12T15:26:43.449733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:43.700737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
conductor 307451
72.3%
otra 63302
 
14.9%
del 10406
 
2.4%
falla 5512
 
1.3%
vehículo 5512
 
1.3%
mala 4894
 
1.2%
condición 4894
 
1.2%
camino 4894
 
1.2%
peatón 4865
 
1.1%
o 4865
 
1.1%
Other values (3) 8497
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 643564
19.2%
r 379250
11.3%
t 377434
11.3%
c 331277
9.9%
n 326998
9.8%
d 324567
9.7%
u 312963
9.3%
C 309267
9.2%
a 105419
 
3.1%
O 63302
 
1.9%
Other values (16) 179553
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2928502
87.3%
Uppercase Letter 387840
 
11.6%
Space Separator 37252
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 643564
22.0%
r 379250
13.0%
t 377434
12.9%
c 331277
11.3%
n 326998
11.2%
d 324567
11.1%
u 312963
10.7%
a 105419
 
3.6%
l 31836
 
1.1%
e 29280
 
1.0%
Other values (10) 65914
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
C 309267
79.7%
O 63302
 
16.3%
F 5512
 
1.4%
M 4894
 
1.3%
P 4865
 
1.3%
Space Separator
ValueCountFrequency (%)
37252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3316342
98.9%
Common 37252
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 643564
19.4%
r 379250
11.4%
t 377434
11.4%
c 331277
10.0%
n 326998
9.9%
d 324567
9.8%
u 312963
9.4%
C 309267
9.3%
a 105419
 
3.2%
O 63302
 
1.9%
Other values (15) 142301
 
4.3%
Common
ValueCountFrequency (%)
37252
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3338323
99.5%
None 15271
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 643564
19.3%
r 379250
11.4%
t 377434
11.3%
c 331277
9.9%
n 326998
9.8%
d 324567
9.7%
u 312963
9.4%
C 309267
9.3%
a 105419
 
3.2%
O 63302
 
1.9%
Other values (14) 164282
 
4.9%
None
ValueCountFrequency (%)
ó 9759
63.9%
í 5512
36.1%

CAPAROD
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Pavimentada
353423 
No Pavimentada
 
32601
Certificado cero
 
1816

Length

Max length16
Median length11
Mean length11.275585
Min length11

Characters and Unicode

Total characters4373123
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPavimentada
2nd rowNo Pavimentada
3rd rowNo Pavimentada
4th rowNo Pavimentada
5th rowNo Pavimentada

Common Values

ValueCountFrequency (%)
Pavimentada 353423
91.1%
No Pavimentada 32601
 
8.4%
Certificado cero 1816
 
0.5%

Length

2022-12-12T15:26:43.985734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:44.214744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pavimentada 386024
91.4%
no 32601
 
7.7%
certificado 1816
 
0.4%
cero 1816
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 1159888
26.5%
i 389656
 
8.9%
e 389656
 
8.9%
t 387840
 
8.9%
d 387840
 
8.9%
P 386024
 
8.8%
v 386024
 
8.8%
m 386024
 
8.8%
n 386024
 
8.8%
o 36233
 
0.8%
Other values (6) 77914
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3918265
89.6%
Uppercase Letter 420441
 
9.6%
Space Separator 34417
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1159888
29.6%
i 389656
 
9.9%
e 389656
 
9.9%
t 387840
 
9.9%
d 387840
 
9.9%
v 386024
 
9.9%
m 386024
 
9.9%
n 386024
 
9.9%
o 36233
 
0.9%
r 3632
 
0.1%
Other values (2) 5448
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 386024
91.8%
N 32601
 
7.8%
C 1816
 
0.4%
Space Separator
ValueCountFrequency (%)
34417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4338706
99.2%
Common 34417
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1159888
26.7%
i 389656
 
9.0%
e 389656
 
9.0%
t 387840
 
8.9%
d 387840
 
8.9%
P 386024
 
8.9%
v 386024
 
8.9%
m 386024
 
8.9%
n 386024
 
8.9%
o 36233
 
0.8%
Other values (5) 43497
 
1.0%
Common
ValueCountFrequency (%)
34417
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4373123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1159888
26.5%
i 389656
 
8.9%
e 389656
 
8.9%
t 387840
 
8.9%
d 387840
 
8.9%
P 386024
 
8.8%
v 386024
 
8.8%
m 386024
 
8.8%
n 386024
 
8.8%
o 36233
 
0.8%
Other values (6) 77914
 
1.8%

SEXO
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Hombre
289419 
Mujer
54141 
Se fugó
42464 
Certificado cero
 
1816

Length

Max length16
Median length6
Mean length6.0167157
Min length5

Characters and Unicode

Total characters2333523
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHombre
2nd rowSe fugó
3rd rowHombre
4th rowSe fugó
5th rowHombre

Common Values

ValueCountFrequency (%)
Hombre 289419
74.6%
Mujer 54141
 
14.0%
Se fugó 42464
 
10.9%
Certificado cero 1816
 
0.5%

Length

2022-12-12T15:26:44.399736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:44.608774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
hombre 289419
67.0%
mujer 54141
 
12.5%
se 42464
 
9.8%
fugó 42464
 
9.8%
certificado 1816
 
0.4%
cero 1816
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 389656
16.7%
r 347192
14.9%
o 293051
12.6%
H 289419
12.4%
m 289419
12.4%
b 289419
12.4%
u 96605
 
4.1%
M 54141
 
2.3%
j 54141
 
2.3%
f 44280
 
1.9%
Other values (10) 186200
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1901403
81.5%
Uppercase Letter 387840
 
16.6%
Space Separator 44280
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 389656
20.5%
r 347192
18.3%
o 293051
15.4%
m 289419
15.2%
b 289419
15.2%
u 96605
 
5.1%
j 54141
 
2.8%
f 44280
 
2.3%
g 42464
 
2.2%
ó 42464
 
2.2%
Other values (5) 12712
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
H 289419
74.6%
M 54141
 
14.0%
S 42464
 
10.9%
C 1816
 
0.5%
Space Separator
ValueCountFrequency (%)
44280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2289243
98.1%
Common 44280
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 389656
17.0%
r 347192
15.2%
o 293051
12.8%
H 289419
12.6%
m 289419
12.6%
b 289419
12.6%
u 96605
 
4.2%
M 54141
 
2.4%
j 54141
 
2.4%
f 44280
 
1.9%
Other values (9) 141920
 
6.2%
Common
ValueCountFrequency (%)
44280
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2291059
98.2%
None 42464
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 389656
17.0%
r 347192
15.2%
o 293051
12.8%
H 289419
12.6%
m 289419
12.6%
b 289419
12.6%
u 96605
 
4.2%
M 54141
 
2.4%
j 54141
 
2.4%
f 44280
 
1.9%
Other values (9) 143736
 
6.3%
None
ValueCountFrequency (%)
ó 42464
100.0%

ALIENTO
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
No
237627 
Se ignora
95239 
53158 
Certificado cero
 
1816

Length

Max length16
Median length2
Mean length3.784491
Min length2

Characters and Unicode

Total characters1467777
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSe ignora
2nd rowSe ignora
3rd rowNo
4th rowSe ignora
5th rowSe ignora

Common Values

ValueCountFrequency (%)
No 237627
61.3%
Se ignora 95239
24.6%
53158
 
13.7%
Certificado cero 1816
 
0.5%

Length

2022-12-12T15:26:44.799738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:45.044739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
no 237627
49.0%
se 95239
19.6%
ignora 95239
19.6%
53158
 
11.0%
certificado 1816
 
0.4%
cero 1816
 
0.4%

Most occurring characters

ValueCountFrequency (%)
o 336498
22.9%
N 237627
16.2%
S 148397
10.1%
e 98871
 
6.7%
i 98871
 
6.7%
r 98871
 
6.7%
97055
 
6.6%
a 97055
 
6.6%
g 95239
 
6.5%
n 95239
 
6.5%
Other values (6) 64054
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 982882
67.0%
Uppercase Letter 387840
 
26.4%
Space Separator 97055
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 336498
34.2%
e 98871
 
10.1%
i 98871
 
10.1%
r 98871
 
10.1%
a 97055
 
9.9%
g 95239
 
9.7%
n 95239
 
9.7%
í 53158
 
5.4%
c 3632
 
0.4%
t 1816
 
0.2%
Other values (2) 3632
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 237627
61.3%
S 148397
38.3%
C 1816
 
0.5%
Space Separator
ValueCountFrequency (%)
97055
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1370722
93.4%
Common 97055
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 336498
24.5%
N 237627
17.3%
S 148397
10.8%
e 98871
 
7.2%
i 98871
 
7.2%
r 98871
 
7.2%
a 97055
 
7.1%
g 95239
 
6.9%
n 95239
 
6.9%
í 53158
 
3.9%
Other values (5) 10896
 
0.8%
Common
ValueCountFrequency (%)
97055
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1414619
96.4%
None 53158
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 336498
23.8%
N 237627
16.8%
S 148397
10.5%
e 98871
 
7.0%
i 98871
 
7.0%
r 98871
 
7.0%
97055
 
6.9%
a 97055
 
6.9%
g 95239
 
6.7%
n 95239
 
6.7%
Other values (5) 10896
 
0.8%
None
ValueCountFrequency (%)
í 53158
100.0%

CINTURON
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Se ignora
219064 
No
97002 
69958 
Certificado cero
 
1816

Length

Max length16
Median length9
Mean length6.0193688
Min length2

Characters and Unicode

Total characters2334552
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSe ignora
2nd rowSe ignora
3rd rowSe ignora
4th rowSe ignora
5th rowSe ignora

Common Values

ValueCountFrequency (%)
Se ignora 219064
56.5%
No 97002
25.0%
69958
 
18.0%
Certificado cero 1816
 
0.5%

Length

2022-12-12T15:26:45.237736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:45.469734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
se 219064
36.0%
ignora 219064
36.0%
no 97002
15.9%
69958
 
11.5%
certificado 1816
 
0.3%
cero 1816
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 319698
13.7%
S 289022
12.4%
e 222696
9.5%
i 222696
9.5%
r 222696
9.5%
220880
9.5%
a 220880
9.5%
g 219064
9.4%
n 219064
9.4%
N 97002
 
4.2%
Other values (6) 80854
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1725832
73.9%
Uppercase Letter 387840
 
16.6%
Space Separator 220880
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 319698
18.5%
e 222696
12.9%
i 222696
12.9%
r 222696
12.9%
a 220880
12.8%
g 219064
12.7%
n 219064
12.7%
í 69958
 
4.1%
c 3632
 
0.2%
t 1816
 
0.1%
Other values (2) 3632
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
S 289022
74.5%
N 97002
 
25.0%
C 1816
 
0.5%
Space Separator
ValueCountFrequency (%)
220880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2113672
90.5%
Common 220880
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 319698
15.1%
S 289022
13.7%
e 222696
10.5%
i 222696
10.5%
r 222696
10.5%
a 220880
10.5%
g 219064
10.4%
n 219064
10.4%
N 97002
 
4.6%
í 69958
 
3.3%
Other values (5) 10896
 
0.5%
Common
ValueCountFrequency (%)
220880
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2264594
97.0%
None 69958
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 319698
14.1%
S 289022
12.8%
e 222696
9.8%
i 222696
9.8%
r 222696
9.8%
220880
9.8%
a 220880
9.8%
g 219064
9.7%
n 219064
9.7%
N 97002
 
4.3%
Other values (5) 10896
 
0.5%
None
ValueCountFrequency (%)
í 69958
100.0%

ID_EDAD
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct88
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.306363
Minimum0
Maximum99
Zeros44618
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:45.693736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median26
Q339
95-th percentile64
Maximum99
Range99
Interquartile range (IQR)21

Descriptive statistics

Standard deviation19.384586
Coefficient of variation (CV)0.6614463
Kurtosis2.2141281
Mean29.306363
Median Absolute Deviation (MAD)8
Skewness1.0961063
Sum11366180
Variance375.76216
MonotonicityNot monotonic
2022-12-12T15:26:45.941097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 72086
 
18.6%
0 44618
 
11.5%
25 9716
 
2.5%
28 9693
 
2.5%
23 9421
 
2.4%
22 9336
 
2.4%
30 9016
 
2.3%
26 8840
 
2.3%
24 8810
 
2.3%
21 8762
 
2.3%
Other values (78) 197542
50.9%
ValueCountFrequency (%)
0 44618
11.5%
12 443
 
0.1%
13 387
 
0.1%
14 840
 
0.2%
15 1739
 
0.4%
16 2948
 
0.8%
17 5167
 
1.3%
18 72086
18.6%
19 8023
 
2.1%
20 8653
 
2.2%
ValueCountFrequency (%)
99 8285
2.1%
98 1
 
< 0.1%
96 2
 
< 0.1%
95 2
 
< 0.1%
94 3
 
< 0.1%
93 4
 
< 0.1%
92 5
 
< 0.1%
91 6
 
< 0.1%
90 13
 
< 0.1%
89 23
 
< 0.1%

CONDMUERTO
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0066625413
Minimum0
Maximum7
Zeros385345
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:46.138116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.085009816
Coefficient of variation (CV)12.759368
Kurtosis345.97869
Mean0.0066625413
Median Absolute Deviation (MAD)0
Skewness14.940908
Sum2584
Variance0.0072266688
MonotonicityNot monotonic
2022-12-12T15:26:46.301094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 385345
99.4%
1 2421
 
0.6%
2 66
 
< 0.1%
3 5
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 385345
99.4%
1 2421
 
0.6%
2 66
 
< 0.1%
3 5
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
< 0.1%
2 66
 
< 0.1%
1 2421
 
0.6%
0 385345
99.4%

CONDHERIDO
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18472566
Minimum0
Maximum30
Zeros322149
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:46.493098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43017411
Coefficient of variation (CV)2.3287187
Kurtosis65.749162
Mean0.18472566
Median Absolute Deviation (MAD)0
Skewness3.1466823
Sum71644
Variance0.18504976
MonotonicityNot monotonic
2022-12-12T15:26:46.665151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 322149
83.1%
1 59982
 
15.5%
2 5542
 
1.4%
3 139
 
< 0.1%
4 17
 
< 0.1%
5 6
 
< 0.1%
30 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 322149
83.1%
1 59982
 
15.5%
2 5542
 
1.4%
3 139
 
< 0.1%
4 17
 
< 0.1%
5 6
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
5 6
 
< 0.1%
4 17
 
< 0.1%
3 139
 
< 0.1%
2 5542
 
1.4%
1 59982
15.5%

PASAMUERTO
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0047700083
Minimum0
Maximum11
Zeros386408
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:46.844289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.093508134
Coefficient of variation (CV)19.603349
Kurtosis2305.0477
Mean0.0047700083
Median Absolute Deviation (MAD)0
Skewness36.220117
Sum1850
Variance0.0087437712
MonotonicityNot monotonic
2022-12-12T15:26:47.017285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 386408
99.6%
1 1169
 
0.3%
2 189
 
< 0.1%
3 40
 
< 0.1%
4 18
 
< 0.1%
6 5
 
< 0.1%
5 4
 
< 0.1%
10 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 386408
99.6%
1 1169
 
0.3%
2 189
 
< 0.1%
3 40
 
< 0.1%
4 18
 
< 0.1%
5 4
 
< 0.1%
6 5
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 5
 
< 0.1%
5 4
 
< 0.1%
4 18
 
< 0.1%
3 40
 
< 0.1%
2 189
 
< 0.1%
1 1169
0.3%

PASAHERIDO
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17838284
Minimum0
Maximum57
Zeros342606
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:47.214288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum57
Range57
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.64312653
Coefficient of variation (CV)3.6053162
Kurtosis464.54866
Mean0.17838284
Median Absolute Deviation (MAD)0
Skewness11.437853
Sum69184
Variance0.41361174
MonotonicityNot monotonic
2022-12-12T15:26:47.592293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 342606
88.3%
1 31338
 
8.1%
2 8698
 
2.2%
3 2935
 
0.8%
4 1403
 
0.4%
5 393
 
0.1%
6 171
 
< 0.1%
7 93
 
< 0.1%
8 60
 
< 0.1%
10 42
 
< 0.1%
Other values (22) 101
 
< 0.1%
ValueCountFrequency (%)
0 342606
88.3%
1 31338
 
8.1%
2 8698
 
2.2%
3 2935
 
0.8%
4 1403
 
0.4%
5 393
 
0.1%
6 171
 
< 0.1%
7 93
 
< 0.1%
8 60
 
< 0.1%
9 32
 
< 0.1%
ValueCountFrequency (%)
57 1
< 0.1%
49 1
< 0.1%
43 1
< 0.1%
37 2
< 0.1%
30 1
< 0.1%
29 1
< 0.1%
27 1
< 0.1%
24 2
< 0.1%
23 1
< 0.1%
22 1
< 0.1%

PEATMUERTO
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
386342 
1
 
1477
2
 
18
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387840
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 386342
99.6%
1 1477
 
0.4%
2 18
 
< 0.1%
3 3
 
< 0.1%

Length

2022-12-12T15:26:48.185287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:48.807289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 386342
99.6%
1 1477
 
0.4%
2 18
 
< 0.1%
3 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 386342
99.6%
1 1477
 
0.4%
2 18
 
< 0.1%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 387840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 386342
99.6%
1 1477
 
0.4%
2 18
 
< 0.1%
3 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 387840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 386342
99.6%
1 1477
 
0.4%
2 18
 
< 0.1%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 386342
99.6%
1 1477
 
0.4%
2 18
 
< 0.1%
3 3
 
< 0.1%

PEATHERIDO
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.060622937
Minimum0
Maximum10
Zeros365679
Zeros (%)94.3%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:49.498289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25672021
Coefficient of variation (CV)4.2347043
Kurtosis47.748743
Mean0.060622937
Median Absolute Deviation (MAD)0
Skewness5.1382863
Sum23512
Variance0.065905269
MonotonicityNot monotonic
2022-12-12T15:26:49.676286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 365679
94.3%
1 21036
 
5.4%
2 971
 
0.3%
3 116
 
< 0.1%
4 24
 
< 0.1%
5 9
 
< 0.1%
10 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 365679
94.3%
1 21036
 
5.4%
2 971
 
0.3%
3 116
 
< 0.1%
4 24
 
< 0.1%
5 9
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
5 9
 
< 0.1%
4 24
 
< 0.1%
3 116
 
< 0.1%
2 971
 
0.3%
1 21036
 
5.4%
0 365679
94.3%

CICLMUERTO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
387325 
1
 
512
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387840
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 387325
99.9%
1 512
 
0.1%
2 3
 
< 0.1%

Length

2022-12-12T15:26:49.933285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:50.211286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 387325
99.9%
1 512
 
0.1%
2 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 387325
99.9%
1 512
 
0.1%
2 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 387840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 387325
99.9%
1 512
 
0.1%
2 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 387840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 387325
99.9%
1 512
 
0.1%
2 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 387325
99.9%
1 512
 
0.1%
2 3
 
< 0.1%

CICLHERIDO
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
379844 
1
 
7902
2
 
91
3
 
2
-1
 
1

Length

Max length2
Median length1
Mean length1.0000026
Min length1

Characters and Unicode

Total characters387841
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 379844
97.9%
1 7902
 
2.0%
2 91
 
< 0.1%
3 2
 
< 0.1%
-1 1
 
< 0.1%

Length

2022-12-12T15:26:50.383287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:50.677284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 379844
97.9%
1 7903
 
2.0%
2 91
 
< 0.1%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 379844
97.9%
1 7903
 
2.0%
2 91
 
< 0.1%
3 2
 
< 0.1%
- 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 387840
> 99.9%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 379844
97.9%
1 7903
 
2.0%
2 91
 
< 0.1%
3 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387841
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 379844
97.9%
1 7903
 
2.0%
2 91
 
< 0.1%
3 2
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387841
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 379844
97.9%
1 7903
 
2.0%
2 91
 
< 0.1%
3 2
 
< 0.1%
- 1
 
< 0.1%

OTROMUERTO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
387732 
1
 
101
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387840
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 387732
> 99.9%
1 101
 
< 0.1%
2 7
 
< 0.1%

Length

2022-12-12T15:26:50.865289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:51.143282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 387732
> 99.9%
1 101
 
< 0.1%
2 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 387732
> 99.9%
1 101
 
< 0.1%
2 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 387840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 387732
> 99.9%
1 101
 
< 0.1%
2 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 387840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 387732
> 99.9%
1 101
 
< 0.1%
2 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 387732
> 99.9%
1 101
 
< 0.1%
2 7
 
< 0.1%

OTROHERIDO
Real number (ℝ)

SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0046462459
Minimum0
Maximum12
Zeros386332
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:51.294288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.083183152
Coefficient of variation (CV)17.903304
Kurtosis2230.0438
Mean0.0046462459
Median Absolute Deviation (MAD)0
Skewness31.449668
Sum1802
Variance0.0069194368
MonotonicityNot monotonic
2022-12-12T15:26:51.527286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 386332
99.6%
1 1272
 
0.3%
2 207
 
0.1%
3 18
 
< 0.1%
4 7
 
< 0.1%
12 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 386332
99.6%
1 1272
 
0.3%
2 207
 
0.1%
3 18
 
< 0.1%
4 7
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
4 7
 
< 0.1%
3 18
 
< 0.1%
2 207
 
0.1%
1 1272
 
0.3%
0 386332
99.6%

NEMUERTO
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
387840 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387840
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 387840
100.0%

Length

2022-12-12T15:26:51.732285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:51.934289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 387840
100.0%

Most occurring characters

ValueCountFrequency (%)
0 387840
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 387840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 387840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 387840
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 387840
100.0%

NEHERIDO
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
387840 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387840
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 387840
100.0%

Length

2022-12-12T15:26:52.131289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-12T15:26:52.313283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 387840
100.0%

Most occurring characters

ValueCountFrequency (%)
0 387840
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 387840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 387840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 387840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 387840
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 387840
100.0%

Total_Muertos
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016988965
Minimum0
Maximum11
Zeros382142
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2022-12-12T15:26:52.452291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15544209
Coefficient of variation (CV)9.1495915
Kurtosis424.21875
Mean0.016988965
Median Absolute Deviation (MAD)0
Skewness14.899592
Sum6589
Variance0.024162242
MonotonicityNot monotonic
2022-12-12T15:26:52.663289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 382142
98.5%
1 5088
 
1.3%
2 455
 
0.1%
3 95
 
< 0.1%
4 31
 
< 0.1%
5 13
 
< 0.1%
6 8
 
< 0.1%
8 3
 
< 0.1%
10 2
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 382142
98.5%
1 5088
 
1.3%
2 455
 
0.1%
3 95
 
< 0.1%
4 31
 
< 0.1%
5 13
 
< 0.1%
6 8
 
< 0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
8 3
 
< 0.1%
7 2
 
< 0.1%
6 8
 
< 0.1%
5 13
 
< 0.1%
4 31
 
< 0.1%
3 95
 
< 0.1%
2 455
 
0.1%
1 5088
1.3%

Total_Heridos
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44923422
Minimum-1
Maximum58
Zeros265447
Zeros (%)68.4%
Negative1
Negative (%)< 0.1%
Memory size3.0 MiB
2022-12-12T15:26:52.867289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum58
Range59
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86643204
Coefficient of variation (CV)1.9286866
Kurtosis153.56822
Mean0.44923422
Median Absolute Deviation (MAD)0
Skewness5.8244887
Sum174231
Variance0.75070447
MonotonicityNot monotonic
2022-12-12T15:26:53.047284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 265447
68.4%
1 90283
 
23.3%
2 21363
 
5.5%
3 6217
 
1.6%
4 2588
 
0.7%
5 1091
 
0.3%
6 400
 
0.1%
7 171
 
< 0.1%
8 88
 
< 0.1%
10 51
 
< 0.1%
Other values (23) 141
 
< 0.1%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 265447
68.4%
1 90283
 
23.3%
2 21363
 
5.5%
3 6217
 
1.6%
4 2588
 
0.7%
5 1091
 
0.3%
6 400
 
0.1%
7 171
 
< 0.1%
8 88
 
< 0.1%
ValueCountFrequency (%)
58 1
< 0.1%
50 1
< 0.1%
44 1
< 0.1%
37 2
< 0.1%
30 2
< 0.1%
29 1
< 0.1%
27 1
< 0.1%
26 1
< 0.1%
24 1
< 0.1%
23 2
< 0.1%

Municipio_Nombre
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Hermosillo
96495 
Cajeme
74650 
San Luis Río Colorado
45061 
Guaymas
30280 
Nogales
28059 
Other values (67)
113295 

Length

Max length29
Median length24
Mean length9.8538727
Min length4

Characters and Unicode

Total characters3821726
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlamos
2nd rowAltar
3rd rowAltar
4th rowAltar
5th rowBaviácora

Common Values

ValueCountFrequency (%)
Hermosillo 96495
24.9%
Cajeme 74650
19.2%
San Luis Río Colorado 45061
11.6%
Guaymas 30280
 
7.8%
Nogales 28059
 
7.2%
Caborca 27470
 
7.1%
Puerto Peñasco 14564
 
3.8%
Navojoa 14214
 
3.7%
Agua Prieta 7105
 
1.8%
Empalme 6886
 
1.8%
Other values (62) 43056
11.1%

Length

2022-12-12T15:26:53.272311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hermosillo 96495
17.0%
cajeme 74650
13.2%
san 47177
8.3%
río 46533
8.2%
luis 45061
8.0%
colorado 45061
8.0%
guaymas 30280
 
5.4%
nogales 28059
 
5.0%
caborca 27470
 
4.9%
puerto 14564
 
2.6%
Other values (83) 110625
19.5%

Most occurring characters

ValueCountFrequency (%)
o 518109
13.6%
a 448024
11.7%
e 345034
 
9.0%
l 288824
 
7.6%
m 225413
 
5.9%
s 225116
 
5.9%
r 213560
 
5.6%
178135
 
4.7%
i 164429
 
4.3%
C 156348
 
4.1%
Other values (41) 1058734
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3080787
80.6%
Uppercase Letter 562804
 
14.7%
Space Separator 178135
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 518109
16.8%
a 448024
14.5%
e 345034
11.2%
l 288824
9.4%
m 225413
7.3%
s 225116
7.3%
r 213560
6.9%
i 164429
 
5.3%
u 114998
 
3.7%
j 94300
 
3.1%
Other values (18) 442980
14.4%
Uppercase Letter
ValueCountFrequency (%)
C 156348
27.8%
H 100675
17.9%
S 49607
 
8.8%
R 47114
 
8.4%
N 45452
 
8.1%
L 45219
 
8.0%
P 38923
 
6.9%
G 34665
 
6.2%
E 13709
 
2.4%
A 11317
 
2.0%
Other values (12) 19775
 
3.5%
Space Separator
ValueCountFrequency (%)
178135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3643591
95.3%
Common 178135
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 518109
14.2%
a 448024
12.3%
e 345034
 
9.5%
l 288824
 
7.9%
m 225413
 
6.2%
s 225116
 
6.2%
r 213560
 
5.9%
i 164429
 
4.5%
C 156348
 
4.3%
u 114998
 
3.2%
Other values (40) 943736
25.9%
Common
ValueCountFrequency (%)
178135
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3748542
98.1%
None 73184
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 518109
13.8%
a 448024
12.0%
e 345034
 
9.2%
l 288824
 
7.7%
m 225413
 
6.0%
s 225116
 
6.0%
r 213560
 
5.7%
178135
 
4.8%
i 164429
 
4.4%
C 156348
 
4.2%
Other values (35) 985550
26.3%
None
ValueCountFrequency (%)
í 51248
70.0%
ñ 14564
 
19.9%
á 6401
 
8.7%
ó 522
 
0.7%
é 343
 
0.5%
ú 106
 
0.1%

Fecha_Formated
Categorical

HIGH CARDINALITY
UNIFORM

Distinct341656
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
 
433
2021-02-28 23:00:00
 
43
2020-04-28 23:00:00
 
42
2020-08-28 23:00:00
 
42
2021-08-28 23:00:00
 
41
Other values (341651)
387239 

Length

Max length19
Median length19
Mean length18.979904
Min length1

Characters and Unicode

Total characters7361166
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305758 ?
Unique (%)78.8%

Sample

1st row1997-01-17 18:00:00
2nd row1997-01-15 17:00:00
3rd row1997-01-30 19:00:00
4th row1997-01-14 03:00:00
5th row1997-01-06 09:00:00

Common Values

ValueCountFrequency (%)
0 433
 
0.1%
2021-02-28 23:00:00 43
 
< 0.1%
2020-04-28 23:00:00 42
 
< 0.1%
2020-08-28 23:00:00 42
 
< 0.1%
2021-08-28 23:00:00 41
 
< 0.1%
2020-03-28 23:00:00 41
 
< 0.1%
2019-03-28 23:00:00 40
 
< 0.1%
2019-02-28 23:00:00 40
 
< 0.1%
2021-06-28 23:00:00 40
 
< 0.1%
2021-01-28 23:00:00 40
 
< 0.1%
Other values (341646) 387038
99.8%

Length

2022-12-12T15:26:53.498286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18:00:00 5174
 
0.7%
14:00:00 4886
 
0.6%
19:00:00 4718
 
0.6%
17:00:00 4655
 
0.6%
20:00:00 4462
 
0.6%
15:00:00 4396
 
0.6%
16:00:00 4394
 
0.6%
13:00:00 4218
 
0.5%
18:30:00 4068
 
0.5%
23:00:00 4049
 
0.5%
Other values (10562) 730227
94.2%

Most occurring characters

ValueCountFrequency (%)
0 2307578
31.3%
1 895342
 
12.2%
2 802541
 
10.9%
- 774814
 
10.5%
: 774814
 
10.5%
387407
 
5.3%
5 255585
 
3.5%
9 254283
 
3.5%
3 249699
 
3.4%
4 192046
 
2.6%
Other values (3) 467057
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5424131
73.7%
Dash Punctuation 774814
 
10.5%
Other Punctuation 774814
 
10.5%
Space Separator 387407
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2307578
42.5%
1 895342
 
16.5%
2 802541
 
14.8%
5 255585
 
4.7%
9 254283
 
4.7%
3 249699
 
4.6%
4 192046
 
3.5%
8 167446
 
3.1%
7 160745
 
3.0%
6 138866
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 774814
100.0%
Other Punctuation
ValueCountFrequency (%)
: 774814
100.0%
Space Separator
ValueCountFrequency (%)
387407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7361166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2307578
31.3%
1 895342
 
12.2%
2 802541
 
10.9%
- 774814
 
10.5%
: 774814
 
10.5%
387407
 
5.3%
5 255585
 
3.5%
9 254283
 
3.5%
3 249699
 
3.4%
4 192046
 
2.6%
Other values (3) 467057
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7361166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2307578
31.3%
1 895342
 
12.2%
2 802541
 
10.9%
- 774814
 
10.5%
: 774814
 
10.5%
387407
 
5.3%
5 255585
 
3.5%
9 254283
 
3.5%
3 249699
 
3.4%
4 192046
 
2.6%
Other values (3) 467057
 
6.3%

Interactions

2022-12-12T15:26:26.292188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:18.560172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:24.099170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:28.352649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:33.302798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:38.104828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:42.597726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:47.867890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:52.966908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:57.929175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:03.446176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:07.501996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:12.344007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:17.379152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:21.666692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:26.548188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:19.101169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:24.349169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:28.612808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:33.549801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:25:38.352726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-12T15:26:12.080003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:17.116113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:21.101194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:26:26.012707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-12T15:26:53.744284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-12T15:26:54.692286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-12T15:26:55.696284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-12T15:26:56.135289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-12T15:26:56.546285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-12T15:26:56.903284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-12T15:26:32.195189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-12T15:26:34.948739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0ID_MUNICIPIOANIOMESID_HORAID_DIADIASEMANAURBANASUBURBANATIPACCIDCAUSAACCICAPARODSEXOALIENTOCINTURONID_EDADCONDMUERTOCONDHERIDOPASAMUERTOPASAHERIDOPEATMUERTOPEATHERIDOCICLMUERTOCICLHERIDOOTROMUERTOOTROHERIDONEMUERTONEHERIDOTotal_MuertosTotal_HeridosMunicipio_NombreFecha_Formated
02075393199711817ViernesSin accidente en esta zonaAccidente en carretera estatalColisión con vehículo automotorConductorPavimentadaHombreSe ignoraSe ignora1800000000000000Alamos1997-01-17 18:00:00
12075404199711715MiércolesSin accidente en esta zonaAccidente en camino ruralVolcaduraConductorNo PavimentadaSe fugóSe ignoraSe ignora000000000000000Altar1997-01-15 17:00:00
22075414199711930JuevesSin accidente en esta zonaAccidente en camino ruralColisión con animalOtraNo PavimentadaHombreNoSe ignora1800000000000000Altar1997-01-30 19:00:00
3207542419971314MartesAccidente en no intersecciónSin accidente en esta zonaColisión con objeto fijoConductorNo PavimentadaSe fugóSe ignoraSe ignora000000000000000Altar1997-01-14 03:00:00
4207543141997196LunesAccidente en intersecciónSin accidente en esta zonaColisión con objeto fijoConductorNo PavimentadaHombreSe ignoraSe ignora1800000000000000Baviácora1997-01-06 09:00:00
520754414199711725SábadoAccidente en intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreSe ignoraSe ignora1800010000000001Baviácora1997-01-25 17:30:00
620754517199711626DomingoAccidente en no intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreSe ignoraSe ignora1800000000000000Caborca1997-01-26 16:30:00
720754617199711627LunesAccidente en no intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreSe ignoraSe ignora1800000000000000Caborca1997-01-27 16:30:00
820754717199711628MartesAccidente en no intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreSe ignoraSe ignora1800000000000000Caborca1997-01-28 16:50:00
920754817199711929MiércolesAccidente en no intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreSe ignoraSe ignora1800000000000000Caborca1997-01-29 19:20:00
Unnamed: 0ID_MUNICIPIOANIOMESID_HORAID_DIADIASEMANAURBANASUBURBANATIPACCIDCAUSAACCICAPARODSEXOALIENTOCINTURONID_EDADCONDMUERTOCONDHERIDOPASAMUERTOPASAHERIDOPEATMUERTOPEATHERIDOCICLMUERTOCICLHERIDOOTROMUERTOOTROHERIDONEMUERTONEHERIDOTotal_MuertosTotal_HeridosMunicipio_NombreFecha_Formated
3878309511216722021111326ViernesAccidente en intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreNoNo2000000000000000San Ignacio Río Muerto2021-11-26 13:15:00
3878319511217722021111929LunesAccidente en intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreNoSe ignora5300000000000000San Ignacio Río Muerto2021-11-29 19:12:00
3878329511218722021112229LunesAccidente en intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreNoNo2900000000000000San Ignacio Río Muerto2021-11-29 22:22:00
3878339511219722021112329LunesAccidente en intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreNoNo3000000000000000San Ignacio Río Muerto2021-11-29 23:47:00
387834951122072202112173ViernesAccidente en intersecciónSin accidente en esta zonaColisión con animalMala condición del caminoNo PavimentadaMujerNoNo4301010000000002San Ignacio Río Muerto2021-12-03 17:42:00
38783595112217220211225DomingoAccidente en intersecciónSin accidente en esta zonaColisión con motocicletaConductorNo PavimentadaHombreNoNo2001010000000002San Ignacio Río Muerto2021-12-05 02:35:00
3878369511222722021122112DomingoAccidente en intersecciónSin accidente en esta zonaColisión con vehículo automotorConductorPavimentadaHombreNoNo2101000000000001San Ignacio Río Muerto2021-12-12 21:39:00
3878379511223722021121525SábadoSin accidente en esta zonaAccidente en camino ruralColisión con objeto fijoConductorPavimentadaHombreNoNo2901000000000001San Ignacio Río Muerto2021-12-25 15:55:00
387838951122472202112826DomingoSin accidente en esta zonaAccidente en camino ruralVolcaduraConductorPavimentadaHombreNoNo2801000000000001San Ignacio Río Muerto2021-12-26 08:59:00
3878399511225722021121726DomingoSin accidente en esta zonaAccidente en camino ruralSalida del caminoConductorPavimentadaHombreNoNo4201010000000002San Ignacio Río Muerto2021-12-26 17:21:00